Nature-inspired algorithms for solving some hard numerical problems

Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and roots of non-linear equations and systems of non-linear equations. Although many iterative methods for finding all the roots of a given function already exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results due to the problem of accumulating rounding errors, (b) good initial approximations to the roots for the algorithm converge, or (c) the computation of first or second order derivatives, which besides being computationally intensive, it is not always possible. The drawbacks previously mentioned served as motivation for the use of Particle Swarm Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are known, respectively, for their ability to explore high-dimensional spaces (not requiring good initial approximations) and for their capability to model complex problems. Besides that, both methods do not need repeated deflations, nor derivative information. The algorithms were described throughout this document and tested using a test suite of hard numerical problems in science and engineering. Results, in turn, were compared with several results available on the literature and with the well-known Durand–Kerner method, depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria. Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem métodos para resolver, de forma óptima, problemas específicos. Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que, tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros de polinómios e raízes de equações não lineares e sistemas de equações não lineares. Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente intensivo, para muitas funções é impossível de se calcular. Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas, respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas. Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem que ambos os algoritmos são capazes de resolver os problemas numéricos considerados

Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO

End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach

Machine Learning based Signal Generation Strategies for High-Speed Optical Transmitters

Optical communication is the only viable solution to respond to the demand for a high bit rate and long transmission distance. Directly modulated lasers (DMLs) are a cheap solution for modulating the light in optical fibre. Moreover, their hardware is simpler than externally modulated lasers. However, DML is inherently chirped and the transmission length with high bit rate is limited. This work explores and implements neural networks based signal predistortion schemes to create transmitters

Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.Comment: 10 pages, 13 figures, 3 table

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.publishedVersio

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Comparative study on the performance of Au/F-TiO2 photocatalyst synthesized from Zamzam water and distilled water under blue light irradiation

Recurring problems of titanium dioxide (TiO2) for needing UV light to be activated and high electron-hole recombination rate limit the application of TiO2 as a prolific photocatalyst. By modifying the morphology and introducing electron trapping species into TiO2, the photocatalytic activity of TiO2 could be improved. Solvents of two different kinds; distilled water and Zamzam water were used in peroxotitanic acid synthesis of TiO2 and the photocatalyst was utilized to degrade Reactive Blue 19 (RB19) dye under blue light irradiation (475 nm) to assess the visible light activity of synthesized TiO2. Fluorine was incorporated to control the morphology while gold nanoparticles (GNP) stabilized by arabic gum were deposited to trap electrons. The morphology of F-TiO2 which appeared to be in ovoid shape was confirmed by Field Emission-Scanning Electron Microscope (FE-SEM) and Transmission Electron Microscope (TEM). Brunauer-Emmett-Teller (BET) surface area and crystallite size estimated from X-ray Diffraction (XRD) data revealed that F-TiO2 modified using HF was smaller in size and exhibited single anatase phase. The band gap of Au-TiO2 synthesized by distilled and Zamzam water was 2.78 eV and 2.89 eV respectively; shifted from 3.08 eV in blank TiO2. Peroxo Au/F-TiO2 synthesized with the incorporation of arabic gum as GNP stabilizer and HF as fluorine modifier degraded up to 49.23% of RB19 within two hours of reaction. The addition of fluorine and gold demonstrated high ability to enhance visible light activity of TiO2 with distilled water used as solvent displayed higher photocatalytic performance compared to Zamzam water

Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals

The breakout success of deep neural networks (NNs) in the 2010's marked a new era in the quest to build artificial intelligence (AI). With NNs as the building block of these systems, excellent performance has been achieved on narrow, well-defined tasks where large amounts of data are available. However, these systems lack certain capabilities that are important for broad use in real-world applications. One such capability is the communication of uncertainty in a NN's predictions and decisions. In applications such as healthcare recommendation or heavy machinery prognostics, it is vital that AI systems be aware of and express their uncertainty – this creates safer, more cautious, and ultimately more useful systems. This thesis explores how to engineer NNs to communicate robust uncertainty estimates on their predictions, whilst minimising the impact on usability. One way to encourage uncertainty estimates to be robust is to adopt the Bayesian framework, which offers a principled approach to handling uncertainty. Two of the major contributions in this thesis relate to Bayesian NNs (BNNs). Specifying appropriate priors is an important step in any Bayesian model, yet it is not clear how to do this in BNNs. The first contribution shows that the connection between BNNs and Gaussian Processes (GPs) provides an effective lens to study BNN priors. NN architectures are derived which mirror the combining of GP kernels to create priors tailored to a task. The second major contribution is a novel way to perform approximate Bayesian inference in BNNs using a modified version of ensembling. Novel analysis improves an understanding of a technique known as randomised MAP sampling. It's shown this is particularly effective when strong correlations exist between parameters, making it well suited to NNs. The third major contribution of the thesis is a non-Bayesian technique that trains a NN to directly output prediction intervals for regression tasks through a tailored objective function. This advances over related works that were incompatible with gradient descent, and ignored one source of uncertainty.EPSRC, Alan Turing Institut